Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry

With the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adve...

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Main Authors: Z. G. Tatarintseva, E. D. Kosmacheva, S. V. Kruchinova, V. A. Akinshina, A. A. Khalafyan
Format: Article
Language:English
Published: Столичная издательская компания 2019-07-01
Series:Рациональная фармакотерапия в кардиологии
Subjects:
Online Access:https://www.rpcardio.online/jour/article/view/1961
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author Z. G. Tatarintseva
E. D. Kosmacheva
S. V. Kruchinova
V. A. Akinshina
A. A. Khalafyan
author_facet Z. G. Tatarintseva
E. D. Kosmacheva
S. V. Kruchinova
V. A. Akinshina
A. A. Khalafyan
author_sort Z. G. Tatarintseva
collection DOAJ
description With the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adverse outcomes in these patients with a view to more careful monitoring of patients in this group.Material and methods. For the construction of predictive models, a statistical method was used for the classification trees and, the procedure for neural networks implemented in the STATISTICA package. For the construction of prognostic models, a sample was used, consisting of 201 patients with and without fatal outcome; condition of each patient was described by 42 quantitative and qualitative clinical indices. Each patient belonged to one of 3 groups according to the type of AF: new-onset AF in ACS patient, paroxysmal AF, documented in an anamnesis before the episode of ACS and the constant or persistent form of AF.Results. To determine predictors of models predicting the possible fatal outcome of a patient, the Spearman correlation coefficient was used. Examination of the correlations for each of the 3 groups separately allowed to reveal clinical indicators for each group – predictors of predictive models with predominantly moderate correlations to the categorical variable “lethal outcome”. After analyzing the prognostic ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment to determine lethal outcome possibility in patients with ACS in the presence of AF using classification trees and neural networks.Conclusion. It is shown that for patients with ACS in the presence of AF, it is possible to construct mathematically based prognostic models that can reliably predict the lethal outcome possibility in patients based on actual values of clinical indices. In this case, clinical indicators can be both quantitative and qualitative (categorical), breaking patients into certain categories. Similar applications, unlike risk scales, are mathematically justified and can form the basis of systems for supporting decision-making.
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spelling doaj.art-ec30676f57af47d8a6c69e66bee5389d2024-04-01T07:43:38ZengСтоличная издательская компанияРациональная фармакотерапия в кардиологии1819-64462225-36532019-07-0115337938510.20996/1819-6446-2019-15-3-379-3851619Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region RegistryZ. G. Tatarintseva0E. D. Kosmacheva1S. V. Kruchinova2V. A. Akinshina3A. A. Khalafyan4Research Institute – Ochapovsky Regional Clinical Hospital No.1Research Institute – Ochapovsky Regional Clinical Hospital No.1Research Institute – Ochapovsky Regional Clinical Hospital No.1Kuban State UniversityKuban State UniversityWith the development of atrial fibrillation (AF), patients with acute coronary syndrome (ACS) are characterized by a twofold increase in the 30-day mortality compared with patients with sinus rhythm. In this regard, there is great interest in developing models of risk stratification to identify adverse outcomes in these patients with a view to more careful monitoring of patients in this group.Material and methods. For the construction of predictive models, a statistical method was used for the classification trees and, the procedure for neural networks implemented in the STATISTICA package. For the construction of prognostic models, a sample was used, consisting of 201 patients with and without fatal outcome; condition of each patient was described by 42 quantitative and qualitative clinical indices. Each patient belonged to one of 3 groups according to the type of AF: new-onset AF in ACS patient, paroxysmal AF, documented in an anamnesis before the episode of ACS and the constant or persistent form of AF.Results. To determine predictors of models predicting the possible fatal outcome of a patient, the Spearman correlation coefficient was used. Examination of the correlations for each of the 3 groups separately allowed to reveal clinical indicators for each group – predictors of predictive models with predominantly moderate correlations to the categorical variable “lethal outcome”. After analyzing the prognostic ability of the developed models, a software module was created in the Microsoft Visual C # 2015 programming environment to determine lethal outcome possibility in patients with ACS in the presence of AF using classification trees and neural networks.Conclusion. It is shown that for patients with ACS in the presence of AF, it is possible to construct mathematically based prognostic models that can reliably predict the lethal outcome possibility in patients based on actual values of clinical indices. In this case, clinical indicators can be both quantitative and qualitative (categorical), breaking patients into certain categories. Similar applications, unlike risk scales, are mathematically justified and can form the basis of systems for supporting decision-making.https://www.rpcardio.online/jour/article/view/1961acute coronary syndromeatrial fibrillationregisterprediction of lethality
spellingShingle Z. G. Tatarintseva
E. D. Kosmacheva
S. V. Kruchinova
V. A. Akinshina
A. A. Khalafyan
Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry
Рациональная фармакотерапия в кардиологии
acute coronary syndrome
atrial fibrillation
register
prediction of lethality
title Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry
title_full Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry
title_fullStr Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry
title_full_unstemmed Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry
title_short Predicting Methods for Analyzing Data on Fatal Outcome Possibility in the Combination of Acute Coronary Syndrome and Atrial Fibrillation According to the Krasnodar Region Registry
title_sort predicting methods for analyzing data on fatal outcome possibility in the combination of acute coronary syndrome and atrial fibrillation according to the krasnodar region registry
topic acute coronary syndrome
atrial fibrillation
register
prediction of lethality
url https://www.rpcardio.online/jour/article/view/1961
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